System Dynamics Modeling of Employee Turnover Among Canadian Nursing Home Workers

Canada had one of the highest Covid-19 death rates worldwide in individuals living in nursing homes (NHs). In response, the province of Ontario with the largest population in Canada passed “The Fixing Long-Term Care Act” in 2021 to address long-standing issues in long-term care NHs. These issues include chronic underfunding, understaffing and poor working conditions that were compounded by increasing resident care needs. As a result, changes were made that included mandatory minimum hours of direct nursing and personal support worker (PSW) care, increased fines for NHs failing to meet standards, and an emphasis on resident rights for holistic care. Despite the Act’s passage, problems retaining and recruiting PSWs make achieving the Act’s commitments difficult. PSWs provide more than 80% of direct care to NH residents in Canada, helping with activities of daily living and providing emotional support and companionship. In Ontario, half of the PSW workforce leave the healthcare sector within 5 years and 43% left due to burnout. High PSW turnover disrupts care continuity and is associated with poorer quality of care and quality of life for residents. Contributing factors to PSW burnout include heavy workloads, systemic disrespect and underappreciation, and feelings of professional inefficacy. The Covid-19 epidemic further exposed the unaddressed need for a systemic approach to the long-standing issues that prevent better resident care and outcomes. The objective of this study was to develop a system dynamics (SD) model that could be used to help design more successful workforce-related policies to reduce PSW turnover and improve overall quality of care in NHs. We developed an SD model of PSW turnover consisting of four sectors: 1. The ministry-level sector that models the government’s policies for direct hours of care, funding, care standards, compliance and enforcement. 2. The management-level sector that models organizational workplace culture and policies for PSW scheduling, backfilling, hiring, on-the-job training and upskilling. 3. The resident-family sector that tracks the flow of residents, their care needs, and family perceptions and expectations of care delivery. 4. The PSW responses sector that models the ways PSWs deal with challenging working conditions. We used causal loop diagrams to describe the complex feedback processes that lead to PSW turnover. One such process explores staff shortages leading to overwork/fatigue exacerbated by increasing resident complexity, productivity and quality declines from hiring new PSWs and employing temporary relief workers, and pervasive disrespect for the PSW profession that locks PSWs into a vicious cycle of “learned helplessness”. Publicly reported NH data and those from the literature, PSW experiences extracted from group model building exercises, and historical home-specific data were used to build and calibrate the model for 4 NHs in Toronto, Ontario. The model is generalizable to an individual NH within and across provinces due to the similar working conditions in Canadian NHs. We discuss the results of several simulations that might reduce PSW turnover and improve care quality and their implications for governmental legislative changes and NH organizational practices and policies. We close with recommendations for policy design to reduce PSW turnover.

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